discretize.utils.mkvc(x, n_dims=1, **kwargs)[source]#

Creates a vector with specified dimensionality.

This function converts a numpy.ndarray to a vector. In general, the output vector has a dimension of 1. However, the dimensionality can be specified if the user intends to carry out a dot product with a higher order array.


An array that will be reorganized and output as a vector. The input array will be flattened on input in Fortran order.


The dimension of the output vector. numpy.newaxis are appened to the output array until it has this many axes.


The output vector, with at least n_dims axes.


Here, we reorganize a simple 2D array as a vector and demonstrate the impact of the n_dim argument.

>>> from discretize.utils import mkvc
>>> import numpy as np
>>> a = np.random.rand(3, 2)
>>> a
array([[0.33534155, 0.25334363],
       [0.07147884, 0.81080958],
       [0.85892774, 0.74357806]])
>>> v = mkvc(a)
>>> v
array([0.33534155, 0.07147884, 0.85892774, 0.25334363, 0.81080958,

In Higher dimensions:

>>> for ii in range(1, 4):
...     v = mkvc(a, ii)
...     print('Shape of output with n_dim =', ii, ': ', v.shape)
Shape of output with n_dim = 1 :  (6,)
Shape of output with n_dim = 2 :  (6, 1)
Shape of output with n_dim = 3 :  (6, 1, 1)

Galleries and Tutorials using discretize.utils.mkvc#

Basic: PlotImage

Basic: PlotImage

Basic: PlotImage
Tree Meshes

Tree Meshes

Tree Meshes
Advection-Diffusion Equation

Advection-Diffusion Equation

Advection-Diffusion Equation